Computes confidence intervals for a population standardized mean difference in a 2-group nonexperimental design with stratified random sampling (a random sample of a specified size from each subpopulation) using a square root weighted variance standardizer or single group standard deviation standardizer. Equality of variances is not assumed.

ci.stdmean.strat(alpha, m1, m2, sd1, sd2, n1, n2, p1)

Arguments

alpha

alpha level for 1-alpha confidence

m1

estimated mean for group 1

m2

estimated mean for group 2

sd1

estimated standard deviation for group 1

sd2

estimated standard deviation for group 2

n1

sample size for group 1

n2

sample size for group 2

p1

proportion of total population in subpopulation 1

Value

Returns a 3-row matrix. The columns are:

  • Estimate - estimated standardized mean difference

  • adj Estimate - bias adjusted standardized mean difference estimate

  • SE - standard error

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

References

Bonett DG (2020). “Point-biserial correlation: Interval estimation, hypothesis testing, meta-analysis, and sample size determination.” British Journal of Mathematical and Statistical Psychology, 73(S1), 113--144. ISSN 0007-1102, doi:10.1111/bmsp.12189 .

Examples

ci.stdmean.strat(.05, 33.2, 30.8, 10.5, 11.2, 200, 200, .533)
#>                         Estimate adj Estimate         SE         LL        UL
#> Weighted standardizer: 0.2215549    0.2211371 0.10052057 0.02453817 0.4185716
#> Group 1 standardizer:  0.2285714    0.2277089 0.10427785 0.02419059 0.4329523
#> Group 2 standardizer:  0.2142857    0.2277089 0.09776049 0.02267868 0.4058927

# Should return:
#                         Estimate  adj Estimate         SE         LL        UL
# Weighted standardizer: 0.2215549     0.2211371 0.10052057 0.02453817 0.4185716
# Group 1 standardizer:  0.2285714     0.2277089 0.10427785 0.02419059 0.4329523
# Group 2 standardizer:  0.2142857     0.2277089 0.09776049 0.02267868 0.4058927